UBS says retail sales data could tip the balance between a 25bp or 50bp rate cut:
- Retail sales and industrial production data… Weakness in these data could affect the Fed’s decision to cut the federal funds rate by 50 basis points instead of 25 basis points
I’m not entirely sure that one data point (or two) could possibly be that significant (note: I’m a 25 year old). But if so, check this out.
For example, if retail sales are significantly above consensus you will call for 25 basis points, and if they are significantly below you will call for 50 basis points.
Consensus forecasts are shown in the table below. This image is taken from ForexLive’s economic data calendar, You can access it here..
For retail sales M/M:
For retail sales excluding automobiles monthly:
And while I’m here, for industrial production m/m
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Why is it important to know such ranges?
Data results that fall outside of market expectations tend to move markets more significantly for several reasons:
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Surprise factor: Markets often price their expectations based on past forecasts and trends. When data deviates significantly from these expectations, it creates a surprise effect. This can lead to a rapid revaluation of assets as investors and traders reassess their positions based on new information.
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Psychological Impact: Investors and traders are influenced by psychological factors. Extreme data points can trigger strong emotional reactions, leading to exaggerated market reactions. This can amplify market movements, especially in the short term.
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Reassessment of risk: Unexpected data may lead to a reassessment of risk. If the data performance is significantly below or above expectations, this may change the perceived risk of some investments. For example, better-than-expected economic data may reduce the perceived risk of a stock investment, leading to a market rally.
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Automated Trading: In today’s markets, a large portion of trading is done by algorithms. These automated systems often have pre-defined conditions or limits, which when triggered by unexpected data can lead to large-scale buying or selling.
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Impact on monetary and fiscal policies: Data that differs significantly from expectations can influence the policies of central banks and governments.
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Liquidity and Market Depth: In some cases, extreme data points can impact market liquidity. If the data is unexpected enough, it can create a temporary imbalance between buyers and sellers, causing larger market movements until a new equilibrium is found.
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Chain Reactions and Correlations: Financial markets are interconnected. A large move in one market or asset class due to unexpected data can lead to correlated moves in other markets, amplifying the overall market impact.